SphereFace [1]} refers to angularly distributed feature embeddings designed for face recognition. In contrast to employing Euclidean margin to the CNN’s learned features, SphereFace proposes to use an angular softmax (named A-Softmax) to learn discriminative face features with a novel geometric interpretation. A-Softmax loss can be interpreted as constraining learned features to be discriminative on a hypersphere manifold, which intrinsically matches the prior that face images lie on a manifold. We use the 512-dimensional SphereFace embeddings.
